The present invention relates to an audio signal compression and decompression, and more specifically to a method of increasing the transmission efficiency and of decreasing the capacity of data storage medium, by compressing audio data markedly. The method can be applied to a technical field for handling audio signals such as broadcasting, moving picture, telephone, TV conference, audio package, audio TV game, etc.
Conventionally, various high efficiency coding methods for audio signals have been so far proposed, and adopted to various audio systems.
For instance, there have been known ADPCM (Adaptive Delta Pulse Code Modulation) method, sub-band coding method, orthogonal transform method, linear prediction method, variable sampling method, etc. In these methods, various algorithms have been proposed and executed to increase the transmission efficiency of audio signals, by removing the redundancy of the quantity of audio data and by compressing audio data to be transmitted by utilization of the human auditory characteristics in such a way that the distortion can be noticed at as small a level as possible.
Here, in the case of the ADPCM method, an adaptive quantization step is introduced to a differential PCM method for using the preceding sampled value as a predicted value, and a difference between the predicted signal value and the true signal value is adaptively quantized without directly quantizing the signal, which is now widely used for recording a message by a multi-function telephone set.
In the case of the sub-band coding method, the audio signals are divided into a number of sub-bands, and each divided sub-band is quantized by the smallest possible number of bits by utilization of the auditory characteristics of each sub-band.
In the case of the orthogonal transform method, the audio signals are separated away at regular time intervals (time window: 5 to 50 msec); after having been transformed into frequency ranges in accordance with discrete Fourier transformation (DFT) or discrete cosine transformation (DCT), the audio signals are divided into groups corresponding to a critical band width; and only the predominant components of the spectrum are quantized by the smallest possible number of bits under consideration of the masking effect.
In the case of the linear prediction method, the predicted values are obtained in accordance with a spectrum analysis based upon a mathematical model (self recursive model) such that the sampled value can be represented by a linear combination of the past sampled value and the present sampled value; and a difference between the predicted sampled value and the true sampled value is coded.
Further, in the case of the variable sampling method, the sampled frequency is changed by utilization of the fact that the upper band limit of audio signals changes.
On the other hand, various methods have been so far proposed and executed with respect to video signal coding. When roughly classified on the basis of the algorithm, there are PCM coding method, prediction coding method, orthogonal transform coding method, hybrid coding method, etc. Further, various practical ways have been proposed for each method.
In the case of video signals, since picture is constructed by a number of scanning lines, there exist such features that the correlation between the adjacent pixels is large in the right and left direction and in the upper and lower direction and further the correlation between two frames is extremely strong in relatively motionless picture.
In other words, it is possible to code the video signals effectively in two dimensions (the intra-frame) and further between three dimensions (the inter-frame) as well as the one dimension (the scanning lines). Further, the video signal band can be compressed largely in accordance with prediction coding (.DELTA.M method, DPC method, etc.) or the orthogonal transform coding (Adamarl transform coding, Fourier transform coding, cosine transform coding, etc.). In addition, it is also possible to improve the coding efficiency of moving picture markedly by executing the motion compensation prediction such as MPEG (Moving Picture Image Coding Experts Groups).
On the other hand, audio signals are of analog asynchronous signals of one dimension from the standpoint of the nature. Therefore, even if there exists a similarity between the adjacent phonemes, when the audio signals are divided into signal group blocks, there exists no correlation between the divided blocks.
Therefore, in the above-mentioned conventional high efficiency coding methods for audio signals, the redundancy of audio signals is only removed by utilization of the predictability based upon the similarity between two adjacent phonemes or upon several phonemes existing immediately before the time band. Or else, audio signals to be transmitted are only compressed by masking for unitizing the auditory characteristics. Therefore, it is impossible to compress audio signals markedly at a high efficiency, being different from video signals.